License plate detection and recognition system

ABSTRACT

A license plate detection and recognition system receives training data comprising images of license plates. The system prepares ground truth data from the training data based predefined parameters. The system trains a first machine learning algorithm based on the ground truth data to generate a license plate detection model. The license plate detection model is configured to detect one or more regions in the images. The one or more regions contains a candidate for a license plate. The LPDR system generates a bounding box for each region. The LPDR system trains a second machine learning algorithm based on the ground truth data and the license plate detection model to generate a license plate recognition model. The license plate recognition model generates a sequence of alphanumeric characters with a level of recognition confidence for the sequence.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional applicationSer. No. 17/317,505, filed May 11, 2021, which is a continuation of U.S.Non-Provisional application Ser. No. 16/272,556, filed Feb. 11, 2019,which claims the benefit of priority of U.S. Provisional Application No.62/629,031, filed Feb. 11, 2018, which are hereby incorporated byreference herein in their entirety.

BACKGROUND

The present application generally relates to the field of license platedetection and recognition, and in particular, relates to methods andsystems for forming and using an efficient and accurate License PlateDetection and Recognition (LPDR) system.

License plate detection and recognition (LPDR) is a technology thatgenerally uses license plate detection approaches followed by opticalcharacter recognition (OCR) on images to read vehicle registrationplates to identify the license plate identifiers. LPDR systems can beused in many day-to-day activities. For example, the LPDR systems mayimprove user's experience by allowing them to pass toll booths non-stop,by automatically determining in/out timestamps of vehicles in parkinglots, and by automatically determining vehicles of condominium membersfor automatic gates opening. The LPDR systems are further helpful infinding a stolen vehicle by searching for license plates detected bypolice car cameras.

Presently, most LPDR systems have been developed using pure computervision-based techniques such as morphology-based character segmentationwith machine learning based Optical Character Recognition (OCR).However, these computer vision-based techniques may properly operate insimple situations: a license plate is clearly and fully visible, theorientation of the license plate from a horizontal line does not exceed25 degrees, there is no or minimal tilt, and a minimal size of thelicense plate is greater than 100 pixels in width. The accuracy ofexisting LPDR systems may be compromised in complex situations: whenthere are shadows, noise, and dust over the license plate. Furthermore,existing LPDR systems may not provide accurate results when the licenseplate is partially overlapped with other plate, include stacked letters,display low contrast data, and the data in the license plate is poorlysegmentable.

Moreover, for live video data detected by police car or traffic cameras,running a modern real-time license recognition model is computationallyexpensive and usually requires powerful hardware such as a GraphicalProcessing Module (GPU). Many a times, the real-time license recognitionhas to be performed by edge devices that lack GPU and have limitedprocessor capacity, and are highly constrained by weight and poweravailability.

In view of the above, there is a need for a license plate recognitionmethod and system that has an improved accuracy in the above-mentionedcomplex situations. The license plate recognition method and systemshould be able to generate accurate results when the license plateinclude stacked letters, display low contrast data, and the data thereinis poorly segmentable. The LPDR system should allow for smoothobject-recognition output on less powerful hardware such as edge devicesand small computers that lack Graphic processing modules (GPUs), so asto save computational resources and electricity costs, and thereforeachieve longer operating time, especially on battery operated portabledevices.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexample embodiments.

FIG. 2 illustrates an example operation of a system for capturing andrecognizing license plates in accordance with one embodiment.

FIG. 3 is a block diagram illustrating an example of a license platedetection and recognition (LPDR) system (LPDR) in accordance with oneexample embodiment.

FIG. 4 illustrates a ground truth (GT) preparation module for trainingthe LPDR system of FIG. 3 , in accordance with one example embodiment.

FIG. 5 illustrates preparation of LP detection module and LP recognitionmodule for forming the LPDR system of FIG. 3 , in accordance with oneexample embodiment.

FIG. 6 is a flow diagram illustrating a method for training learningsubmodules of the LPDR system of FIG. 3 , in accordance with an exampleembodiment.

FIG. 7 is a flow diagram illustrating a method for using the LPDR systemof FIG. 3 , in accordance with an example embodiment.

FIG. 8 is a flow diagram illustrating a method for using the LPDR systemof FIG. 3 , in accordance with another example embodiment.

FIG. 9 illustrates a routine in accordance with one embodiment.

FIG. 10 illustrates an exemplary input image that includes a singlelicense plate in accordance with one example embodiment.

FIG. 11 illustrates an exemplary input image that includes a singlelicense plate in accordance with one example embodiment.

FIG. 12 illustrates exemplary LP regions received by the LP recognitionmodule of FIG. 3 for training.

FIG. 13 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, according to an example embodiment.

DETAILED DESCRIPTION

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors 1004) may be configured by software (e.g., anapplication 916 or application portion) as a hardware component thatoperates to perform certain operations as described herein. A hardwarecomponent may also be implemented mechanically, electronically, or anysuitable combination thereof. For example, a hardware component mayinclude dedicated circuitry or logic that is permanently configured toperform certain operations. A hardware component may be aspecial-purpose processor, such as a field-programmable gate array(FPGA) or an application specific integrated circuit (ASIC). A hardwarecomponent may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. Forexample, a hardware component may include software executed by ageneral-purpose processor or other programmable processor. Onceconfigured by such software, hardware components become specificmachines (or specific components of a machine 1000) uniquely tailored toperform the configured functions and are no longer general-purposeprocessors 1004. It will be appreciated that the decision to implement ahardware component mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware), may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time. Hardwarecomponents can provide information to, and receive information from,other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information). The various operationsof example methods described herein may be performed, at leastpartially, by one or more processors that are temporarily configured(e.g., by software) or permanently configured to perform the relevantoperations. Whether temporarily or permanently configured, suchprocessors may constitute processor-implemented components that operateto perform one or more operations or functions described herein. As usedherein, “processor-implemented component” refers to a hardware componentimplemented using one or more processors. Similarly, the methodsdescribed herein may be at least partially processor-implemented, with aparticular processor or processors being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors 1004 or processor-implemented components.Moreover, the one or more processors may also operate to supportperformance of the relevant operations in a “cloud computing”environment or as a “software as a service” (SaaS). For example, atleast some of the operations may be performed by a group of computers(as examples of machines including processors), with these operationsbeing accessible via a network (e.g., the Internet) and via one or moreappropriate interfaces (e.g., an API). The performance of certain of theoperations may be distributed among the processors, not only residingwithin a single machine, but deployed across a number of machines. Insome example embodiments, the processors or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexample embodiments, the processors or processor-implemented componentsmay be distributed across a number of geographic locations.

“Communication Network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Machine-Storage Medium” refers to a single or multiple storage devicesand/or media (e.g., a centralized or distributed database, and/orassociated caches and servers) that store executable instructions,routines and/or data. The term shall accordingly be taken to include,but not be limited to, solid-state memories, and optical and magneticmedia, including memory internal or external to processors. Specificexamples of machine-storage media, computer-storage media and/ordevice-storage media include non-volatile memory, including by way ofexample semiconductor memory devices, e.g., erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), FPGA, and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks The terms “machine-storage medium,”“device-storage medium,” “computer-storage medium” mean the same thingand may be used interchangeably in this disclosure. The terms“machine-storage media,” “computer-storage media,” and “device-storagemedia” specifically exclude carrier waves, modulated data signals, andother such media, at least some of which are covered under the term“signal medium.”

“Processor” refers to any circuit or virtual circuit (a physical circuitemulated by logic executing on an actual processor) that manipulatesdata values according to control signals (e.g., “commands”, “op codes”,“machine code”, etc.) and which produces corresponding output signalsthat are applied to operate a machine. A processor may, for example, bea Central Processing Unit (CPU), a Reduced Instruction Set Computing(RISC) processor, a Complex Instruction Set Computing (CISC) processor,a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), anApplication Specific Integrated Circuit (ASIC), a Radio-FrequencyIntegrated Circuit (RFIC) or any combination thereof. A processor mayfurther be a multi-core processor having two or more independentprocessors (sometimes referred to as “cores”) that may executeinstructions contemporaneously.

“Carrier Signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Signal Medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

“Computer-Readable Medium” refers to both machine-storage media andtransmission media. Thus, the terms include both storage devices/mediaand carrier waves/modulated data signals. The terms “machine-readablemedium,” “computer-readable medium” and “device-readable medium” meanthe same thing and may be used interchangeably in this disclosure.

Example methods and systems are directed to a detection and recognitionlicense plate system. Examples merely typify possible variations. Unlessexplicitly stated otherwise, components and functions are optional andmay be combined or subdivided, and operations may vary in sequence or becombined or subdivided. In the following description, for purposes ofexplanation, numerous specific details are set forth to provide athorough understanding of example embodiments. It will be evident to oneskilled in the art, however, that the present subject matter may bepracticed without these specific details.

The present application describes devices/machines/systems that identifya vehicle license plate (LP) in an image and recognize the characters inthe license plate. The presently described system provides a highaccuracy solution for license plate detection and recognition (LPDR)system. In one example embodiment, the LPDR system includes two separateLP detection and recognition modules to allow to split processing. Forexample, the detection part may be performed on one device/platformwhile the recognition part may be performed another device/platform. TheLP detection and recognition modules may be built on top of aConvolutional Neural Network (CNN) model, and allow for control ofperformance/accuracy of detection and recognition. In one exampleembodiment, the LPDR system is based on a combination of multiple deeplearning neural network sub-systems for detection and optical characterrecognition, and enables higher quality, effectiveness and accuracy,high performance and scalability.

The presently described LPDR system can handle more complex casesincluding processing of partially visible LPs, LPs with stacked letters,low contrast data, and poorly segmentable LPs, among other poor visibleconditions. In one example, the LPDR system allows to perform accuraterecognition for single, multi-row and stacked license plates. In anotherexample, the LPDR system recognizes stacked letters in license plates.

In one example embodiment, the present application describes a methodfor detecting and recognizing license plates with a LPDR system. TheLPDR system receives training data comprising images of license plates;prepares ground truth data from the training data based predefinedparameters; trains a first machine learning algorithm based on theground truth data to generate a license plate detection model. Thelicense plate detection model is configured to detect one or moreregions in the images. The one or more regions contains a candidate fora license plate. The LPDR system generates a bounding box for eachregion. The LPDR system trains a second machine learning algorithm basedon the ground truth data and the license plate detection model togenerate a license plate recognition model. The license platerecognition model generates a sequence of alphanumeric characters with alevel of recognition confidence for the sequence.

FIG. 1 is a diagrammatic representation of a network environment 100 inwhich some example embodiments of the present disclosure may beimplemented or deployed.

One or more application servers 104 provide server-side functionalityvia a network 102 to a networked user device (in the form of a clientdevice 106 of the user 128) and a streaming system 130. A web client 110(e.g., a browser) and a programmatic client 108 (e.g., an “app”) arehosted and execute on the client device 106. The client device 106 cancommunicate with a streaming system 130 and application servers 104 viathe network 102 or via other wireless or wired means. The streamingsystem 130 comprises a video server 132 and video capturing devices 134.The video capturing devices 134 generate image/video data and providesthe image/video data to the video server 132.

An Application Program Interface (API) server 118 and a web server 120provide respective programmatic and web interfaces to applicationservers 104. A specific application server 116 hosts a license platedetection and recognition system 122 that operates with the applicationserver 116. In one example, the license plate detection and recognitionsystem 122 receives video or image data from the streaming system 130,detects license plates in the images or video frames from the streamingsystem 130, and recognizes the alphanumeric identifiers in the detectedlicense plates. The operations performed by the license plate detectionand recognition system 122 may be also performed or distributed toanother server such as a third-party server 112. For example, thedetection of license plates may be performed at the license platedetection and recognition system 122 and the recognition of licenseplates may be performed at the third-party server 112.

The web client 110 communicates with the license plate detection andrecognition system 122 via the web interface supported by the web server120. Similarly, the programmatic client 108 communicates with thelicense plate detection and recognition system 122 via the programmaticinterface provided by the Application Program Interface (API) server118. The third-party application 114 may, for example, be a anotherapplication to support the license plate detection and recognitionsystem 122 or mine the data from the license plate detection andrecognition system 122. For example, the third-party application 114 mayaccess location information, registration information, and otherinformation related to the cars with the identified license plates. Theapplication server 116 is shown to be communicatively coupled todatabase servers 124 that facilitates access to an information storagerepository or databases 126. In an example embodiment, the databases 126includes storage devices that store information to be published and/orprocessed by the license plate detection and recognition system 122.

FIG. 2 illustrates an example operation of a system for capturing andrecognizing license plates in accordance with one embodiment. The videocapturing devices 134 includes camera 202 and camera 204 that generateimage/video data of the cars 206, 208, 210, and their correspondinglicense plates 212, 214, and 216. The video capturing devices 134 may beinstalled across multiple locations. Examples of locations include, butare not limited to, roads, parking spaces, garages, toll booths, outsideresidential areas, outside office spaces, outside public places (such asmalls, recreational areas, museums, libraries, hospitals, policestations, fire stations, schools, colleges), and the like. Examples ofthe video capturing devices 134 include, but are not limited to,Closed-Circuit Television (CCTVs) cameras, High Definition (HD) cameras,non-HD cameras, handheld cameras, traffic cameras, police car cameras,cameras on unmanned aerial vehicles (UAVs) or any other video/imagegrabbing modules.

The video server 132 receives a dynamic imagery or a video footage fromthe video capturing devices 134, and may transmit the associated data tothe license plate detection and recognition system 122. A video/imagearchive (not shown) is a data storage that is configured to storepre-recorded or archived videos/images. The video/image archive may becomposed of a plurality of local databases or remote databases. Also,the databases may be centralized and/or distributed. In an alternatescenario, the video/image archive may store data using a cloud basedscheme. Similar to the video server 132, the video/image archive maytransmit data to the license plate detection and recognition system 122.

In one example, the video server 132 communicates the image/video datato the license plate detection and recognition system 122 for furtherprocessing. In another example embodiment, the detection and recognitionof the license plates may be performed either at the license platedetection and recognition system 122 or at the video server 132 or acombination of both.

In one example, the license plate detection and recognition system 122may be part of at least one of a surveillance system, a security system,a traffic monitoring system, a home security system, and a toll feesystem. The license plate detection and recognition system 122 may beconfigured to receive data from at least one of: video server 132, thevideo/image archive, and/or client device 106. The data may be in formof one or more video streams and/or one or more images. In case of theone or more video streams, the license plate detection and recognitionsystem 122 may convert each stream into a plurality of static images orframes. The license plate detection and recognition system 122 mayprocess the one or more received images (or static image frames ofvideos) and execute a license plate detection technique. In thedetection technique, the one or more images may be analyzed and one ormore regions containing vehicle license plates may be detected. For eachlicense plate, the license plate detection and recognition system 122may recognize the characters that make up the vehiclelicense/registration number.

In an example embodiment, the video capturing devices 134, the licenseplate detection and recognition system 122 may be integrated in a singledevice, where the single device is either a portable smartphone having abuilt-in camera and a display, or an integrated LPDR device.

In another example embodiment, the license plate detection andrecognition system 122 may be a custom LPDR recognition server softwareto provide real-time license plate detection and recognition for allcameras on a local network.

In yet another example embodiment, the license plate detection andrecognition system 122 may be a processing device that does not includea GPU, and includes limited CPU capabilities to run license platedetection and recognition processes. The license plate detection andrecognition system 122 is described in more detail below with respect toFIG. 3 .

FIG. 3 is a block diagram illustrating an example of a license platedetection and recognition (LPDR) system (LPDR) in accordance with oneexample embodiment. The license plate detection and recognition system122 includes an image input module 302 for receiving an inputimage/video, an LP detection module 304 for detecting a license plate inthe input image/video, and an LP recognition module 306 for recognizinga license plate identifier in the detected license plate, and displayingthe LP data on an associated display device.

The image input module 302 is configured to receive data from at leastone of: the video server 132, the video/image archive, the client device106, and the third-party server 112. The data may be in form of one ormore video streams and/or one or more images. In case of the one or morevideo streams, the image input module 302 may convert each stream into aplurality of static images or frames.

In one example embodiment, the license plate detection and recognitionsystem 122 enables a user to specify the following parameters beforeprocessing the input image:

-   -   a country identifier    -   processing mode: either a single image or video stream mode    -   performance and accuracy profile: depending on required quality        of processing, it is possible to switch to more accurate but        more CPU consuming profile or go with a profile which is well        balanced between high accuracy and performance    -   multi-core support mode: depending on need, processing can be        done using a single, several or all available cores of the        license plate detection and recognition system 122.

The LP detection module 304 is configured to analyze the input imagefrom the image input module 302 and to identify one or more LP regions,such that each LP region includes an image of a license plate. Withreference to FIG. 10 , an image 1002 is received by the image inputmodule 302, and is transferred to the LP detection module 304, such thatthe LP detection module 304 detects a license plate region 1004containing a license plate.

In one example embodiment, the LP detection module 304 may be built ontop of a Convolutional Neural Network (CNN) based technology, where theCNN is a machine learning model related to computer vision and imageprocessing that deals with detecting instances of semantic objects of acertain class in digital images and videos. In one example, the LPdetection module 304 may be implemented using a Single Shot Multi-boxdetector (SSD) or a DetectNet architecture that has been chosen as adefault baseline, and optimized for accuracy and performance aspects, byadjusting a number of layers, an input image size, and classificationlayers.

In another example embodiment, the LP detection module 304 may receive acolor image (3 channel) as an input image, and then passes the inputimage through multiple computation layers to detect one or more LPregions that are most probable to be license plates. Each LP region mayinclude a license plate, and the license plates detected in the imagemay be of different sizes.

In another example embodiment, the LP detection module 304 detects thecoordinates of bounding boxes of multiple license plates in an image.The LP detection module 304 detects and marks the bounding boxes of thelicense plates in such a way that the bounding boxes when displayed onthe display device mostly do not overlap with each other. The LPdetection module 304 is configured to detect and report all clearlyvisible LPs of expected sizes. Thus, based on the detection, the LPdetection module 304 may return an array of LP regions, referred to asLP_CANDIDATES_ARRAY {BBox, Type}, where for each LP region, the LPdetection module 304 may return coordinates of corresponding boundingboxes, and a type of the LP region. The type specifies, if the detectedLP region is a one row license plate, or a multiple row license plate.

The LP detection module 304 is further configured to filter the array ofLP regions to remove the duplicate LP regions, and also lessprobable/false LP regions, to generate a filtered array of LP regions,referred to as LP_CANDIDATES_FILTERED_ARRAY {BBox, Type}. The process offiltering significantly reduces the total number of LP regions, therebyreducing the processing time, and increasing the overall efficiency ofthe license plate detection and recognition system 122.

The LP recognition module 306 may be built on the top of a CNN, and isconfigured to perform segmentation and recognition operations. In anexample embodiment, the LP recognition module 306 is configured toreceive the filtered array of LP candidate regions from the LP detectionmodule 304, and process each LP region based on its type.

In one example embodiment, the LP recognition module 306 may implementthe following three phase processing to process the filtered array of LPcandidate regions:

Firstly, for each LP region, the LP recognition module 306 may use afirst type of CNN such as STN based CNN model to determine theparameters required for a first affine transformation from a currentstate/representation to a horizontally oriented license plate. In anexample, for each LP candidate region, the LP recognition module 306 maycompute an angle needed for rotation of corresponding license region toput the corresponding license plate in a horizontally aligned state, ifthe license plate is not in the horizontally aligned state.

Secondly, the LP recognition module 306 may rotate one or more LPregions by corresponding computed angles, to keep the license plates ofcorresponding LP regions in a horizontally aligned state. This stepsignificantly reduces complexity of corresponding CNN and improvesoverall recognition accuracy as well.

Finally, depending on required level of accuracy and type, the LPrecognition module 306 may use a second type of CNN model such as ResNetor STN based model (that has densenet-style layers) for simultaneoussegmentation and recognition of text data for each LP candidate region.The LP recognition module 306 may perform end-to-end segmentation andrecognition for a given horizontally oriented LP candidate, andrecognize corresponding LP identifier as a sequence of alphanumericcharacters. Thereafter, the LP recognition module 306 returns the LPidentifier accompanied with a level of recognition confidence.

In one example embodiment, the license plate detection and recognitionsystem 122 may include a LP tracking module (not shown) to use arecognized LP identifier of an input image to update the recognized LPidentifier of a previous input image, when the input image is a part ofan input video stream.

Thus, with the given approach and training, the LP recognition module306 may perform very accurate recognition for single, multi-row andstacked license plates. The success rate of recognition may be more than95%, if an input image of a license plate was taken in more or lessreasonable conditions, or at least a human may detect and read such alicense plate without difficulty.

In another example embodiment, the license plate detection andrecognition system 122 may transmit the LP recognition results to othercomponents for further processing, storage, or such as the userinterface for display. In an example, the coordinates of bounding boxesand license plate identifiers of recognized LPs may be sent as a messagealong with the video frames, to display labels and/or bounding boxes onlive video streams on an associated display device. In one embodiment,the license plate detection and recognition system 122 may generate avideo output for display in which bounding boxes are drawn arounddetected license plates, along with the recognized license identifier.

Although, the license plate detection and recognition system 122 isshown to have three modules, it would be apparent to one of ordinaryskill in the art that the license plate detection and recognition system122 may add more sub modules and neural networks to support additionalcountries, states, regions, and applications, where combination ofnumeral and character based signs on the license plates can besuccessfully detected and recognized.

In one example embodiment, the license plate detection and recognitionsystem 122 may include a LP location detection module (not shown) thatdoes automatic determination of a registered country/state ofcorresponding vehicle, based on content of a detected license plate.

FIG. 4 illustrates a ground truth (GT) preparation module for trainingthe LPDR system of FIG. 3 , in accordance with one example embodiment.

The ground truth preparation module 402 prepares specific data neededfor training and validation of different LPDR modules of the licenseplate detection and recognition system 122. The preparation of groundtruth data may be performed prior operating machine learning and deeplearning techniques. The feeding of reasonable data into a trainingprocess allows for control of what the ground truth preparation module402 may interpret as reasonable data to be extracted from an input videoframe or image. In one example embodiment, the ground truth preparationmodule 402 receives real data from a training data database 404, and LPtemplates forms from an LP templates database 406. Thereafter, theground truth preparation module 402 prepares ground truth data for thelicense plate detection and recognition system 122, using LP groundtruth preparation and verification tools, through LP templatepreparation procedure and fonts extraction, to generate an annotatedartificially generated LP database 408, an annotated LP database 410,and a fonts database 412.

In one embodiment, the following information may be fed into the groundtruth preparation module 402 to prepare ground truth for the LPdetection module 304:

-   -   Information regarding types of license plates, for example, one        row license plate, two or multi-row license plate, as the        license plates are distinguishable by the number of rows;    -   Information that the one row license plate detector is being        used to detect a license plate with stacked letters;    -   License plates which have sufficient size and observed for at        least 50% of their width/height. It does not matter if content        of a license plate is clearly visible or blurred, but if it can        be detected by a person easily;    -   Alphanumeric template information accompanied with specific        fonts used for generation of license plates onto training        images;    -   LP bounding box points and number of LP identifier text rows on        it for generation of license plates onto images which are        re-used for further training;

In another example embodiment, the following information may be fed intothe ground truth preparation module 402 to prepare ground truth for theLP recognition module 306:

-   -   Fully-visible and clearly recognizable license plates of        training images    -   One or more templates for every unique license plate format,        where the format usually includes an image of a template of a        license plate without LP identifier    -   A mask image with information about how LP identifier data is        supposed to be put on a template image    -   Information about regions of output for alphanumeric data        accompanied with an output format    -   Fonts and sizes used for drawing characters over a LP template    -   Coordinates of bounding boxes for all license plates of training        images    -   Region/state information of license plates of training images    -   Country information of license plates of training images

FIG. 5 illustrates preparation of LP detection module 304 and LPrecognition module 306 for forming the LPDR system of FIG. 3 , inaccordance with one example embodiment.

In operation, the real LP database 502 is augmented and mixed with someproportion with artificial prepared input data 508 (also referred to asartificial LP samples). The artificial prepared input data 508 isgenerated using LP templates database 514, fonts database 512 andinformation about expected sequences of alpha-numeric based formats ofLP IDs. When the real LP database 502 and the artificial prepared inputdata 508 are merged into a trained LP database 506, then it is used fortraining of the LP recognition module 306. At the same time, accordingto the current process, the real LP database 502 are used for trainingof the LP detection module 304. For validation purposes, some artificialprepared input data from artificial prepared input data 508 and somereal LP data from real trained LP database 510 may be separated from thetraining dataset. However, data from the real trained LP database 510and the artificial prepared input data 508 can be used for blindvalidation of the LP detection module 304 and LP recognition module 306respectively.

FIG. 6 is a flow diagram illustrating a method for training learningsubmodules of the LPDR system of FIG. 3 , in accordance with an exampleembodiment. Operations in the method 600 may be performed by the licenseplate detection and recognition system 122, using components (e.g.,modules, engines) described above with respect to FIG. 3 . Accordingly,the method 600 is described by way of example with reference to thelicense plate detection and recognition system 122. However, it shall beappreciated that at least some of the operations of the method 600 maybe deployed on various other hardware configurations or be performed bysimilar components residing elsewhere.

At block 602, the license plate detection and recognition system 122receives a training set containing images of license plates. At block604, the license plate detection and recognition system 122 preparesground truth based on the training set. At block 606, the license platedetection and recognition system 122 trains a first learning submodulerelated to the detection of license plates based on the ground truth. Atblock 608, the license plate detection and recognition system 122 trainsa second learning submodule related to recognition of characters basedon the ground truth.

FIG. 7 is a flow diagram illustrating a method for using the LPDR systemof FIG. 3 , in accordance with an example embodiment. Operations in themethod 700 may be performed by the license plate detection andrecognition system 122, using components (e.g., modules, engines)described above with respect to FIG. 3 . Accordingly, the method 700 isdescribed by way of example with reference to the license platedetection and recognition system 122. However, it shall be appreciatedthat at least some of the operations of the method 700 may be deployedon various other hardware configurations or be performed by similarcomponents residing elsewhere.

At block 702, the license plate detection and recognition system 122receives an image from the image input module 302. At block 704, thelicense plate detection and recognition system 122 receives a set ofparameters for the image. At block 706, the license plate detection andrecognition system 122 detects license plate candidates in the imageusing LP detection module 304 and form a first set of LP candidates. Atblock 708, the license plate detection and recognition system 122invalidates candidates from the first set of LP candidates that includesthe remaining non-invalidated candidates. At block 710, the licenseplate detection and recognition system 122 generates a string identifierfor each LP in the second set of LP candidates using the LP recognitionmodule 306. At block 712, the license plate detection and recognitionsystem 122 invalidates LP candidates from the second set of LPcandidates based on LP templates and forms a third set of LP candidatesincluding the remaining non-invalidated LP candidates. At block 714, thelicense plate detection and recognition system 122 forms an array ofinformation for each LP in the third set of LP candidates.

FIG. 8 is a flow diagram illustrating a method for using the LPDR systemof FIG. 3 , in accordance with another example embodiment. Operations inthe method 800 may be performed by the license plate detection andrecognition system 122, using components (e.g., modules, engines)described above with respect to FIG. 3 . Accordingly, the method 800 isdescribed by way of example with reference to the license platedetection and recognition system 122. However, it shall be appreciatedthat at least some of the operations of the method 800 may be deployedon various other hardware configurations or be performed by similarcomponents residing elsewhere.

At block 802, an input image is received at the image input module 302.For example, the image input module 302 receives a single snapshot or avideo stream of frames with vehicles moving through a scene. If theimage input module 302 receives a video stream (instead of an image),the video stream may be divided into a sequence of image frames by theimage input module. In an example, the input image is a color image andis at least of D1 resolution.

At block 804, one or more LP regions are detected in the input image bythe LP detection module 304 using a machine learning model. In anexample, the input image is passed through multiple computation layersof a Convolutional Neural Network (CNN) to detect one or more LP regionsthat are most probable to be license plates. Each LP region may be ofdifferent sizes. In one example embodiment, one or more detected LPregions may be invalidated by the LP detection module 304 taking intoaccount the expected LP sizes, mutual arrangement and correspondingconfidence values to obtain a set of validated LP regions.

At block 806, for each detected LP region, coordinates of correspondingbounding box in the input image, type of corresponding LP and acorresponding detection confidence value are returned by the LPdetection module 304 in form of an array. The type of the license plateis selected from at least one of: a single row license plate comprisingone or more characters in a single row or stacked form, and a multiplerow license plate. In an embodiment of the present disclosure, thedetected LP regions may be filtered to remove the duplicate LP regions,and also less probable/false LP regions of the input image.

At block 808, each LP region is rotated by a corresponding pre-computedangle by an LP recognition module 306, using another machine learningmodel, to align each LP region to a horizontally aligned state. In oneembodiment, the LP recognition module 306 includes a CNN based LPrecognizer to determine a rotation angle of each detected LP region toalign the same to a horizontally aligned state, and rotate the LP regionby the determined rotation angle.

At block 810, a sequence of alphanumeric characters of LP identifier ofeach LP region is recognized by the LP recognition module 306 using yetanother machine learning model. In one embodiment, the LP recognitionmodule 306 is provided with native characters support for recognizingthe LP identifier of each LP region. For example, the LP recognitionmodule 306 may use a pre-defined set of native characters of the countrywith whom the LP is registered, in addition to characters of Englishalphabets to recognize the corresponding LP identifier. For differentcountries, the LP recognition module 306 may support customization of LPID content so it may include native letters too. The LPID results may beprovided in a UTF8 form. In another example embodiment, the LPrecognition module 306 performs invalidation of one or more recognizedLP identifiers based on information of minimal/maximal number ofcharacters, expected LP template, and a corresponding recognitionconfidence value.

At block 812, for each LP region, the sequence of recognizedalphanumeric characters is returned by the LP recognition module 306along with a corresponding recognition confidence value. In oneembodiment, an array of license plates is returned that havesuccessfully passed phases of detection and recognition. The arrayincludes a string identifier, a recognition confidence value, andbounding box coordinates of each recognized license plate therein. In anembodiment of the present disclosure, when the input image is part of aninput video stream, the license plate detection and recognition system122 includes a license plate tracking module to perform a look-up forevery detected and recognized LP to check if this LP was reportedbefore. If the LP was reported before, then the previously reported LPis updated with the most recent information (if it is more accurate). Ifthe LP was not reported before, then the current LP is marked with a newLP flag. Thus, the recognized LP identifier of a current input image iscontinuously tracked to update the recognized LP identifier of aprevious input image of the input video stream. In one exampleembodiment, each recognized LP is displayed on the input image in acorresponding bounding box, along with corresponding LP identifier and arecognition confidence value. In another example embodiment, thecoordinates (location) and label (identification) of the license platemay be displayed on live video streams or input images, or may be storedwith corresponding frame, or used for transmitting alerts, or otherpurposes.

In block 902, routine 900 receiving training data comprising images oflicense plates. In block 904, routine 900 preparing ground truth datafrom the training data based predefined parameters. In block 906,routine 900 trains a first machine learning algorithm based on theground truth data to generate a license plate detection model, thelicense plate detection model configured to detect one or more regionsin the images, the one or more regions containing a candidate for alicense plate, and to generate a bounding box for each region. In block908, routine 900 trains a second machine learning algorithm based on theground truth data and the license plate detection model to generate alicense plate recognition model, the license plate recognition modelconfigured to generate a sequence of alphanumeric characters with alevel of recognition confidence for the sequence.

FIG. 10 illustrates an exemplary input image that includes a singlelicense plate in accordance with one example embodiment. The image inputmodule 302 may receive the image 1002. The LP detection module 304detects the license plate region 1004. The LP recognition module 306determines the alphanumeric portion in the license plate license plateregion 1004.

FIG. 11 illustrates an exemplary input image 1102 that includes severallicense plates in accordance with one example embodiment. The LPdetection module 304 detects the coordinates of bounding boxes of themultiple license plates (license plate 1104, license plate 1106, licenseplate 1108). In one example embodiment, the LP detection module 304detects and marks the bounding boxes of the license plates 1104, 1106,1108 in such a way that the bounding boxes when displayed on the displaydevice mostly do not overlap with each other.

Although, three images of license plates 1104, 1106, 1108 areillustrated herein for detection by the LP detection module 304, itwould be apparent to one of ordinary skill in the art, that the LPdetection module 304 is configured to detect and report all clearlyvisible LPs of expected sizes.

FIG. 12 illustrates exemplary LP regions received by the LP recognitionmodule 306 of FIG. 3 for training. In one example, the LP recognitionmodule 306 may rotate the regions 1204, 1206, 1208, and 1210respectively by corresponding predefined angles so as to horizontallyalign the corresponding license plates.

By horizontally aligning the license plates of regions 1204, 1206, 1208,and 1210, the complexity of further training may be reduced, and theoverall license plate training process may be made more robust,effective and accurate. All characters within a license plate now may begenerally placed in approximately the same positions. The training oflicense plates at random rotation and tilt angles may require addingsignificant amount of memory/weights for being able to recognize Licenseplate identifiers (LPID) at random angles.

FIG. 13 is a diagrammatic representation of the machine 1300 withinwhich instructions 1308 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1300to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1308 may cause the machine 1300to execute any one or more of the methods described herein. Theinstructions 1308 transform the general, non-programmed machine 1300into a particular machine 1300 programmed to carry out the described andillustrated functions in the manner described. The machine 1300 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1300 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1300 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a PDA, an entertainment media system, a cellulartelephone, a smart phone, a mobile device, a wearable device (e.g., asmart watch), a smart home device (e.g., a smart appliance), other smartdevices, a web appliance, a network router, a network switch, a networkbridge, or any machine capable of executing the instructions 1308,sequentially or otherwise, that specify actions to be taken by themachine 1300. Further, while only a single machine 1300 is illustrated,the term “machine” shall also be taken to include a collection ofmachines that individually or jointly execute the instructions 1308 toperform any one or more of the methodologies discussed herein.

The machine 1300 may include processors 1302, memory 1304, and I/Ocomponents 1342, which may be configured to communicate with each othervia a bus 1344. In an example embodiment, the processors 1302 (e.g., aCentral Processing Unit (CPU), a Reduced Instruction Set Computing(RISC) processor, a Complex Instruction Set Computing (CISC) processor,a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), anASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, orany suitable combination thereof) may include, for example, a processor1306 and a processor 1310 that execute the instructions 1308. The term“processor” is intended to include multi-core processors that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.13 shows multiple processors 1302, the machine 1300 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory 1304 includes a main memory 1312, a static memory 1314, and astorage unit 1316, both accessible to the processors 1302 via the bus1344. The main memory 1304, the static memory 1314, and storage unit1316 store the instructions 1308 embodying any one or more of themethodologies or functions described herein. The instructions 1308 mayalso reside, completely or partially, within the main memory 1312,within the static memory 1314, within machine-readable medium 1318within the storage unit 1316, within at least one of the processors 1302(e.g., within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1300.

The I/O components 1342 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1342 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1342 mayinclude many other components that are not shown in FIG. 13 . In variousexample embodiments, the I/O components 1342 may include outputcomponents 1328 and input components 1330. The output components 1328may include visual components (e.g., a display such as a plasma displaypanel (PDP), a light emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The inputcomponents 1330 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and/or force of touches or touch gestures, or other tactileinput components), audio input components (e.g., a microphone), and thelike.

In further example embodiments, the I/O components 1342 may includebiometric components 1332, motion components 1334, environmentalcomponents 1336, or position components 1338, among a wide array ofother components. For example, the biometric components 1332 includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1334 includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1336 include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 1338 includelocation sensor components (e.g., a GPS receiver component), altitudesensor components (e.g., altimeters or barometers that detect airpressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1342 further include communication components 1340operable to couple the machine 1300 to a network 1320 or devices 1322via a coupling 1324 and a coupling 1326, respectively. For example, thecommunication components 1340 may include a network interface componentor another suitable device to interface with the network 1320. Infurther examples, the communication components 1340 may include wiredcommunication components, wireless communication components, cellularcommunication components, Near Field Communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), WiFi® components,and other communication components to provide communication via othermodalities. The devices 1322 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1340 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1340 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1340, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., memory 1304, main memory 1312, static memory1314, and/or memory of the processors 1302) and/or storage unit 1316 maystore one or more sets of instructions and data structures (e.g.,software) embodying or used by any one or more of the methodologies orfunctions described herein. These instructions (e.g., the instructions1308), when executed by processors 1302, cause various operations toimplement the disclosed embodiments.

The instructions 1308 may be transmitted or received over the network1320, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components1340) and using any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1308 may be transmitted or received using a transmission medium via thecoupling 1326 (e.g., a peer-to-peer coupling) to the devices 1322.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader scope of the present disclosure. Accordingly, the specificationand drawings are to be regarded in an illustrative rather than arestrictive sense. The accompanying drawings that form a part hereof,show by way of illustration, and not of limitation, specific embodimentsin which the subject matter may be practiced. The embodimentsillustrated are described in sufficient detail to enable those skilledin the art to practice the teachings disclosed herein. Other embodimentsmay be utilized and derived therefrom, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. This Detailed Description, therefore, is not to betaken in a limiting sense, and the scope of various embodiments isdefined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

What is claimed is:
 1. A computer-implemented method comprising:receiving an input image; detecting one or more license plate (LP)regions in the input image using a license plate detection model,wherein each LP region includes a license plate; for each detected LPregion, determining coordinates of a corresponding bounding box in theinput image, a corresponding type of LP, and a corresponding detectionconfidence value; determining a rotation angle for each LP region usinga third machine learning model, the rotation angle configured to aligneach LP region to a horizontally aligned state; rotating each LP regionby a corresponding rotation angle; and determining a sequence ofalphanumeric characters of a LP identifier in each rotated LP regionwith a recognition confidence value corresponding to the sequence, usinga license plate recognition model.
 2. The computer-implemented method ofclaim 1, further comprising: receiving training data comprising imagesof license plates; preparing ground truth data from the training databased on predefined parameters; training a first machine learningalgorithm based on the ground truth data to generate the license platedetection model, the license plate detection model configured to detectone or more regions in the images, the one or more regions containing acandidate for a license plate, and to generate a bounding box for eachlicense plate; and training a second machine learning algorithm based onthe ground truth data and the license plate detection model to generatethe license plate recognition model, the license plate recognition modelconfigured to generate a sequence of alphanumeric characters in thebounding box with a level of recognition confidence for the sequence. 3.The computer-implemented method of claim 2, wherein the predefinedparameters comprise a combination of a number of rows for each licenseplate in the images, a minimum size for each license plate in theimages, a minimum ratio of width-to-height for each license plate in theimages, a minimum visibility for each license plate in the images, atemplate for each license, fonts, and text identifier from each licenseplate in the images.
 4. The computer-implemented method of claim 1,further comprising: determining a bounding box corresponding to an imageof a license plate in the input image by applying the license platedetection model to the input image; and generating a string identifierwith a corresponding confidence level for the license plate in thebounding box by applying the license plate recognition model to theimage of the license plate within the bounding box.
 5. Thecomputer-implemented method of claim 4, wherein generating the stringidentifier comprises: determining a rotation angle of the bounding boxusing a third machine learning algorithm corresponding to the thirdmachine learning model; aligning the image of the license plate in thebounding box to the horizontally aligned state by performing a rotationof the image of the license plate in the bounding box with the rotationangle; and performing a segmentation of the aligned image of the licenseplate to determine the string identifier corresponding to the licenseplate.
 6. The computer-implemented method of claim 1, furthercomprising: determining a bounding box corresponding to an image of eachcandidate license plate in the input image by applying the license platedetection model to the input image, the bounding box indicating alicense plate type and a corresponding confidence level; forming a firstset of candidates based on the determined bounding boxes; firstinvalidating one or more candidates in the first set of candidates inresponse to detecting that a first feature of the one or more candidatesin the first set of candidates exceeds a first feature threshold;forming a second set of candidates of remaining candidates after thefirst invalidating; generating a string identifier with a correspondingconfidence level for each license plate in each bounding box by applyingthe license plate recognition model to the second set of candidates;second invalidating one or more candidates in the second set ofcandidates in response to detecting that a second feature of the one ormore candidates in the second set of candidates exceeds a second featurethreshold; forming a third set of candidates of remaining candidatesafter the second invalidating; and generating an array of license plateinformation comprising an image of a license plate from the input image,a corresponding string identifier, a corresponding bounding boxcoordinates, and a corresponding confidence level.
 7. Thecomputer-implemented method of claim 2, wherein the first machinelearning algorithm includes a first convolution neural network (CNN)based algorithm, wherein the second machine learning algorithm includesa second convolution neural network (CNN) based algorithm, and whereinthe type of the license plate is selected from at least one of: a singlerow license plate comprising one or more characters in a single row or astacked form, and a multiple row license plate.
 8. Thecomputer-implemented method of claim 2, wherein training the firstmachine learning algorithm is based on the ground truth data, whereinthe ground truth data comprises historical data regarding detection of asingle row license plate of stacked letters from one or more images,detection of a multiple row license plate from one or more images,standard size and dimensions of each type of license plate, bounding boxpoints of one or more license plates, and number of correspondinglicense plate identifier text rows for each type of bounding box.
 9. Thecomputer-implemented method of claim 2, wherein training the secondmachine learning algorithm is based on the ground truth data, whereinthe ground truth data comprises historical data regarding one or moreimages of a template of a license plate without identifier, a mask imagewith information about how license plate identifier data is put on atemplate image, and alphanumeric identifiers of one or more licenseplates visible in corresponding images.
 10. The computer-implementedmethod of claim 1, further comprising: validating each detected LPregion before performing the license plate recognition, wherein thevalidation is performed based on an expected size of the license plate,mutual arrangement, and a detection confidence value; validating the LPidentifier of the detected LP region, based on a minimal and maximalnumber of characters of the license plate identifier, an expectedlicense plate template, and a level of recognition confidence value; andusing the LP identifier of a current input image to update the LPidentifier of a previous input image, in response to the current inputimage being a part of an input video stream.
 11. A computing apparatus,the computing apparatus comprising: a processor; and a memory storinginstructions that, when executed by the processor, configure theapparatus to perform operations comprising: receiving an input image;detecting one or more license plate (LP) regions in the input imageusing a license plate detection model, wherein each LP region includes alicense plate; for each detected LP region, determining coordinates of acorresponding bounding box in the input image, a corresponding type ofLP, and a corresponding detection confidence value; determining arotation angle for each LP region using a third machine learning model,the rotation angle configured to align each LP region to a horizontallyaligned state; rotating each LP region by a corresponding rotationangle; and determining a sequence of alphanumeric characters of a LPidentifier in each rotated LP region with a recognition confidence valuecorresponding to the sequence, using a license plate recognition model.12. The computing apparatus of claim 11, wherein the operations furthercomprise: receiving training data comprising images of license plates;preparing ground truth data from the training data based on predefinedparameters; training a first machine learning algorithm based on theground truth data to generate the license plate detection model, thelicense plate detection model configured to detect one or more regionsin the images, the one or more regions containing a candidate for alicense plate, and to generate a bounding box for each license plate;and training a second machine learning algorithm based on the groundtruth data and the license plate detection model to generate the licenseplate recognition model, the license plate recognition model configuredto generate a sequence of alphanumeric characters in the bounding boxwith a level of recognition confidence for the sequence.
 13. Thecomputing apparatus of claim 12, wherein the predefined parameterscomprise a combination of a number of rows for each license plate in theimages, a minimum size for each license plate in the images, a minimumratio of width-to-height for each license plate in the images, a minimumvisibility for each license plate in the images, a template for eachlicense, fonts, and text identifier from each license plate in theimages.
 14. The computing apparatus of claim 11, wherein the operationsfurther comprise: determining a bounding box corresponding to an imageof a license plate in the input image by applying the license platedetection model to the input image; and generating a string identifierwith a corresponding confidence level for the license plate in thebounding box by applying the license plate recognition model to theimage of the license plate within the bounding box.
 15. The computingapparatus of claim 14, wherein generating the string identifiercomprises: determining a rotation angle of the bounding box using athird machine learning algorithm corresponding to the third machinelearning model; aligning the image of the license plate in the boundingbox to the horizontally aligned state by performing a rotation of theimage of the license plate in the bounding box with the rotation angle;and performing a segmentation of the aligned image of the license plateto determine the string identifier corresponding to the license plate.16. The computing apparatus of claim 11, wherein the operations furthercomprise: determining a bounding box corresponding to an image of eachcandidate license plate in the input image by applying the license platedetection model to the input image, the bounding box indicating alicense plate type and a corresponding confidence level; forming a firstset of candidates based on the determined bounding boxes; firstinvalidating one or more candidates in the first set of candidates inresponse to detecting that a first feature of the one or more candidatesin the first set of candidates exceeds a first feature threshold;forming a second set of candidates of remaining candidates after thefirst invalidating; generating a string identifier with a correspondingconfidence level for each license plate in each bounding box by applyingthe license plate recognition model to the second set of candidates;second invalidating one or more candidates in the second set ofcandidates in response to detecting that a second feature of the one ormore candidates in the second set of candidates exceeds a second featurethreshold; forming a third set of candidates of remaining candidatesafter the second invalidating; and generating an array of license plateinformation comprising an image of a license plate from the input image,a corresponding string identifier, a corresponding bounding boxcoordinates, and a corresponding confidence level.
 17. The computingapparatus of claim 12, wherein the first machine learning algorithmincludes a first convolution neural network (CNN) based algorithm,wherein the second machine learning algorithm includes a secondconvolution neural network (CNN) based algorithm, and wherein the typeof the license plate is selected from at least one of: a single rowlicense plate comprising one or more characters in a single row or astacked form, and a multiple row license plate.
 18. The computingapparatus of claim 12, wherein training the first machine learningalgorithm is based on the ground truth data, wherein the ground truthdata comprises historical data regarding detection of a single rowlicense plate of stacked letters from one or more images, detection of amultiple row license plate from one or more images, standard size anddimensions of each type of license plate, bounding box points of one ormore license plates, and number of corresponding license plateidentifier text rows for each type of bounding box.
 19. The computingapparatus of claim 12, wherein training the second machine learningalgorithm is based on the ground truth data, wherein the ground truthdata comprises historical data regarding one or more images of atemplate of a license plate without identifier, a mask image withinformation about how license plate identifier data is put on a templateimage, and alphanumeric identifiers of one or more license platesvisible in corresponding images.
 20. A non-transitory computer-readablestorage medium, the computer-readable storage medium includinginstructions that when executed by a computer, cause the computer toperform operations comprising: receiving an input image; detecting oneor more license plate (LP) regions in the input image using a licenseplate detection model, wherein each LP region includes a license plate;for each detected LP region, determining coordinates of a correspondingbounding box in the input image, a corresponding type of LP, and acorresponding detection confidence value; determining a rotation anglefor each LP region using a third machine learning model, the rotationangle configured to align each LP region to a horizontally alignedstate; rotating each LP region by a corresponding rotation angle; anddetermining a sequence of alphanumeric characters of a LP identifier ineach rotated LP region with a recognition confidence value correspondingto the sequence, using a license plate recognition model.